Overview

Introduction

Enterprise AI Data Scientist is a niche course that targets developers who want to transition their career towards Enterprise AI

The course covers:

Design of Enterprise AI

Technology foundations of Enterprise AI systems

Specific AI use cases

Development of AI services

Deployment and Business models

The course targets developers and Architects who want to transition their career to AI. The course correlates the new AI ideas with familiar concepts like ERP, Data warehousing etc and helps to make the transition easier,

According to Deloitte: by the “end of 2016 more than 80 of the world’s 100 largest enterprise software companies by revenues will have integrated cognitive technologies into their products”. Gartner also predicts that 40 percent of the new investment made by enterprises will be in predictive analytics by 2020. AI is moving fast into the Enterprise and AI developments can create value for the Enterprise.

The Enterprise AI Layer

The course is based on a logical concept called an ‘Enterprise AI layer’. This AI layer is focussed on solving relatively mundane problems which are domain specific for an Enterprise. While this is not as ‘sexy’ as the original vision of AI, it provides tangible and practical benefits to companies. We could see such a layer as an extension to the Data Warehouse or the ERP system(an Intelligent Data Warehouse/ Cognitive ERP system). Thus, the approach provides tangible and practical benefits for the Enterprise with a clear business model. For instance, an organization would transcribe call centre agents’ interactions with customers create a more intelligent workflow, bot etc using Deep learning algorithms.

So, if we imagine such a conceptual AI layer for the enterprise, what does it mean in terms of new services that can be offered by an Enterprise? Here are some examples

Bots :Bots are a great example of the use of AI to automate repetitive tasks like scheduling meetings. Bots are often the starting point of engagement for AI especially in Retail and Financial services

Inferring from textual/voice narrative: Security applications to detect suspicious behaviour, Algorithms that can draw connections between how patients describe their symptoms etc

These applications provide competitive advantage, Differentiation, Customer loyalty and mass personalization for any Enterprise. They have simple business models (such as deployed as premium features /new products /cost reduction )

Course Outline

AI – A conceptual Overview

In this section, we cover the basics of AI and Deep learning. We start with machine learning concepts and relate how Deep Learning/AI fits with them. We explore the workings of Algorithms and the various technologies underpinning AI. AI enables computers to do some things better than humans especially when it comes to finding insights from large amounts of Unstructured or semi-structured data. Technologies like Machine learning , Natural language processing (NLP) , Speech recognition, and computer vision drive the AI layer. More specifically, AI applies to an algorithm which is learning on its own. We explore the design and principles behind these Algorithms.